Application of neural networks, PCA and feature extraction for prediction of nucleotide sequences by using genomic signals

Autor:	Valeri Mladenov, Simona Petrakieva, G. Tsenov, Paul Dan Cristea, Rodica Tuduce
Rok vydání:	2008
Předmět:	Signal processing Artificial neural network Computer science business.industry Feature extraction Genomics Pattern recognition Quantitative Biology::Genomics Genome Signal Principal component analysis Artificial intelligence business Sequence (medicine)
Zdroj:	2008 9th Symposium on Neural Network Applications in Electrical Engineering.
DOI:	10.1109/neurel.2008.4685575
Popis:	Converting symbolic sequences into complex genomic signals reveals surprising regularities of genomes, both locally and at a global scale. This approach allows using signal processing methods for the handling and analysis of nucleotide sequences, specifically for the prediction of nucleotides when knowing the preceding ones in the sequence. In this paper we propose both Feature Extraction (FE) and Principal Component Analysis (PCA) as methods to efficiently extract the major features of a genomic signal, using then a neural network to predict the next sample in the sequence.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::18dafd73b23b1fa4857f78b28a4b9591 https://doi.org/10.1109/neurel.2008.4685575 Zobrazit plný text záznamu